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Informatics united: exemplary studies combining medical informatics, neuroinformatics and bioinformatics.

机译:信息学联合:结合医学信息学,神经信息学和生物信息学的示范性研究。

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OBJECTIVES: Medical informatics, neuroinformatics and bioinformatics provide a wide spectrum of research. Here, we show the great potential of synergies between these research areas on the basis of four exemplary studies where techniques are transferred from one of the disciplines to the other. METHODS: Reviewing and analyzing exemplary and specific projects at the intersection of medical informatics, neuroinformatics, and bioinformatics from our experience in an interdisciplinary research group. RESULTS: Synergy emerges when techniques and solutions from medical informatics, bioinformatics, or neuroinformatics are successfully applied in one of the other disciplines. Synergy was found in 1. the modeling of neurophysiological systems for medical therapy development, 2. the use of image processing techniques from medical computer vision for the analysis of the dynamics of cell nuclei, and 3. the application of neuroinformatics tools for data mining in bioinformatics and as classifiers in clinical oncology. CONCLUSIONS: Each of the three different disciplines have delivered technologies that are readily applicable in the other disciplines. The mutual transfer of knowledge and techniques proved to increase efficiency and accuracy in a manifold of applications. In particular, we expect that clinical decision support systems based on techniques derived from neuro- and bioinformatics have the potential to improve medical diagnostics and will finally lead to a personalized delivery of healthcare.
机译:目的:医学信息学,神经信息学和生物信息学提供广泛的研究。在这里,我们基于四个示范性研究显示了这些研究领域之间协同增效的巨大潜力,这些研究将技术从一个学科转移到另一个学科。方法:根据我们在跨学科研究小组中的经验,回顾和分析医学信息学,神经信息学和生物信息学交叉学科中的示例性项目和特定项目。结果:当医学信息学,生物信息学或神经信息学的技术和解决方案成功应用于其他学科之一时,就会产生协同作用。在以下方面发现了协同作用:1.用于医学治疗开发的神经生理系统建模; 2.使用来自医学计算机视觉的图像处理技术来分析细胞核动力学;以及3.将神经信息学工具用于数据挖掘中。生物信息学和临床肿瘤学分类。结论:三个不同学科中的每一个都提供了易于在其他学科中应用的技术。事实证明,知识和技术的相互转移可以提高多种应用程序的效率和准确性。特别是,我们希望基于源自神经和生物信息学的技术的临床决策支持系统具有改善医疗诊断的潜力,并将最终导致个性化的医疗保健。

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